The computing dendrite cuntz hermann remme michiel w h torben nielsen benjamin
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The book first looks at morphological properties of dendrites and summarizes the approaches to measure dendrite morphology quantitatively and to actually generate synthetic dendrite morphologies in computer models. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Neurons are capable, when the summation of excitatory inputs in dendrites is locally non-linear, to perform linearly non-separable classifications. With this broad focus, an overview is given of the diversity of mechanisms that dendrites can employ to shape neural computations. In der Arbeit von Daniel Lückehe wird ein neues hybrides Verfahren zur Dimensionsreduktion methodisch erarbeitet, analysiert und durch experimentelle assessments mit vorhandenen Methoden verglichen.

In the dendritic threshold non-linear neuron model the dendrites of the neuron can be nonlinear. The book first looks at morphological properties of dendrites and summarizes the approaches to measure dendrite morphology quantitatively and to actually generate synthetic dendrite morphologies in computer models. What role do dendritic morphology and the distributions of synapses and membrane properties over the dendritic tree have in determining the output of a neuron in response to its input? The present paper tackles these problems which could strengthen or weaken the impact of dendrites on computation. This chapter demonstrates that both of these limitations can be overcome in a network of nonlinearly integrating units. Remme, Mate Lengyel and Boris S. The text has several models of the retina, cortex layers 2 and 4, and the meaning for neural function. Most studies focus on how supralinear summation of excitatory synaptic responses arising from clustered inputs within single dendrites result in the enhancement of neuronal firing, enabling simple computations such as feature detection.

In practice, however, some of these functions require synaptic weights of arbitrary large precision. In this article we will review the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing. Moreover, analytical methods show under which conditions the expanded computational capacities are generic, i. Fiete The role of ongoing dendritic oscillations in single-neuron dynamics Michiel W. The second main theme focuses on how dendrites contribute to the computations that neurons perform.

Because the former has not been described experimentally, and the latter requires specific wiring within the network, we will focus here on linearly non-separable functions that can be implemented with only excitatory synapses and monotone dendritic operations. This review discusses the current state of work on General Coordination, Visual Feature-Binding, Variable Binding, and the Subjective Unity of Perception. Description: 1 online resource 350 pages. The circuits are designed, analyzed and verified on circuit boards. The biophysical models of individual neurons are next discussed.

With this wide concentration, an summary is given of the range of mechanisms that dendrites can hire to form neural computations. Roque and Luciano da F. Studying the function of dendritic structures has a long tradition in theoretical neuroscience, starting with the pioneering work by Wilfrid Rall in the 1950s. Remme, Mate Lengyel and Boris S. If this depolarization is bigger than the arithmetic sum, the dendrite is spiking; if the depolarization is smaller, the dendrite is saturating. Comin, Julian Tejada, Matheus P.

Although there are cases local strategies where dendritic operations can dictate the neuronal computation, dendritic operations must be studied and understood in the context of the knowledge of the wiring of specific features onto the dendritic tree. Remme, Mate Lengyel and Boris S. The equivalent structural and mathematical representation of biological neuron is shown in Fig. Series Title: , volume 11. Gutkin Different levels of I h determine distinct temporal integration in bursting and regular-spiking neurons in rat subiculum Ingrid van Welie, Michiel W. Illusory conjunction rates exceeded zero, and were not affected by distance.

We show here that dendrites can alleviate this requirement. The goal of this volume is to provide a resume of the state-of-the-art in experimental, computational, and mathematical investigations into the functions of dendrites in a variety of neural systems. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Remme and Susanne Schreiber In: Springer, New York 2013 Somatic versus dendritic resonance: differential filtering of inputs through non-uniform distributions of active conductances Ekaterina Zhuchkova, Michiel W. This morphological characterization ranges from the study of fractal principles to describe dendrite topologies, to the consequences of optimization principles for dendrite shape. Remme, Máté Lengyel and Boris S.

Recent advances in experimental techniques allow us to study dendrites with a new perspective and in greater detail. Springer Series in Computational Neuroscience, vol 11. The book first looks at morphological homes of dendrites and summarizes the methods to degree dendrite morphology quantitatively and to really generate man made dendrite morphologies in computing device types. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Implications for feature binding models are discussed. It brings together a wide range of studies, with topics ranging from general to system-specific phenomena.

Recent advances in experimental techniques allow us to study dendrites with a new perspective and in greater detail. We also reviewed how such functions can be implemented with either supralinear or sublinear dendrites depending on the spatial mapping of those features within the dendritic tree. This result demonstrates a sharp contrast between artificial neuron models taking into account or not dendrite, as these difficult classifications are easy to implement in a neuron model with a passive dendrite. The studies come from many different neural systems and animal species ranging from invertebrates to mammals. The target of this quantity is to supply a résumé of the cutting-edge in experimental, computational, and mathematical investigations into the features of dendrites in various neural systems.